{"id":"https://openalex.org/W3206699728","doi":"https://doi.org/10.32614/rj-2022-058","title":"CIMTx: An R Package for Causal Inference with Multiple Treatments using Observational Data","display_name":"CIMTx: An R Package for Causal Inference with Multiple Treatments using Observational Data","publication_year":2022,"publication_date":"2022-12-20","ids":{"openalex":"https://openalex.org/W3206699728","doi":"https://doi.org/10.32614/rj-2022-058","mag":"3206699728","pmid":"https://pubmed.ncbi.nlm.nih.gov/39310290","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/11415261"},"language":"en","primary_location":{"id":"doi:10.32614/rj-2022-058","doi":"https://doi.org/10.32614/rj-2022-058","is_oa":true,"landing_page_url":"http://dx.doi.org/10.32614/rj-2022-058","pdf_url":"https://journal.r-project.org/articles/RJ-2022-058/RJ-2022-058.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/I4402554076","host_organization_name":"R Foundation for Statistical Computing","host_organization_lineage":["https://openalex.org/I4402554076"],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The R Journal","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite","pubmed"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journal.r-project.org/articles/RJ-2022-058/RJ-2022-058.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054202053","display_name":"Liangyuan Hu","orcid":"https://orcid.org/0000-0002-4067-892X","observed_orcids":["https://orcid.org/0000-0002-4067-892X"]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]},{"id":"https://openalex.org/I4390039302","display_name":"Rutgers Health","ror":"https://ror.org/029z02k15","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I102322142","https://openalex.org/I4390039302"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Liangyuan Hu","raw_affiliation_strings":["Rutgers University School of Public Health"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University School of Public Health","institution_ids":["https://openalex.org/I102322142","https://openalex.org/I4390039302"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101976912","display_name":"Jiayi Ji","orcid":"https://orcid.org/0000-0002-8641-6183","observed_orcids":["https://orcid.org/0000-0002-8641-6183"]},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]},{"id":"https://openalex.org/I4390039302","display_name":"Rutgers Health","ror":"https://ror.org/029z02k15","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I102322142","https://openalex.org/I4390039302"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiayi Ji","raw_affiliation_strings":["Rutgers University School of Public Health"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University School of Public Health","institution_ids":["https://openalex.org/I102322142","https://openalex.org/I4390039302"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3692,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.52647413,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"14","issue":"3","first_page":"213","last_page":"230"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13398","display_name":"Data Analysis with R","score":0.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.0,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.6940000057220459},{"id":"https://openalex.org/keywords/multiple-treatments","display_name":"multiple treatments","score":0.9940000176429749},{"id":"https://openalex.org/keywords/observational-data","display_name":"observational data","score":0.9129999876022339},{"id":"https://openalex.org/keywords/binary-outcomes","display_name":"binary outcomes","score":0.9900000095367432},{"id":"https://openalex.org/keywords/inverse-probability-of-treatment-weighting","display_name":"inverse probability of treatment weighting","score":0.8989999890327454},{"id":"https://openalex.org/keywords/bayesian-additive-regression-trees","display_name":"Bayesian additive regression trees","score":0.9959999918937683},{"id":"https://openalex.org/keywords/vector-matching","display_name":"vector matching","score":0.6610000133514404},{"id":"https://openalex.org/keywords/targeted-maximum-likelihood-estimation","display_name":"targeted maximum likelihood estimation","score":0.9929999709129333},{"id":"https://openalex.org/keywords/generalized-propensity-score","display_name":"generalized propensity score","score":0.7450000047683716},{"id":"https://openalex.org/keywords/positivity-assumption","display_name":"positivity assumption","score":0.6840000152587891},{"id":"https://openalex.org/keywords/ignorability","display_name":"ignorability","score":0.8899999856948853},{"id":"https://openalex.org/keywords/monte-carlo-sensitivity-analysis","display_name":"Monte Carlo sensitivity analysis","score":0.9570000171661377},{"id":"https://openalex.org/keywords/data-simulation","display_name":"data simulation","score":0.5230000019073486}],"concepts":[{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.7819621562957764},{"id":"https://openalex.org/C2779915747","wikidata":"https://www.wikidata.org/wiki/Q17058619","display_name":"Inverse probability weighting","level":3,"score":0.7614989280700684},{"id":"https://openalex.org/C23131810","wikidata":"https://www.wikidata.org/wiki/Q818574","display_name":"Observational study","level":2,"score":0.670348048210144},{"id":"https://openalex.org/C17923572","wikidata":"https://www.wikidata.org/wiki/Q7250160","display_name":"Propensity score matching","level":2,"score":0.6558368802070618},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6032616496086121},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5113481879234314},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5050135254859924},{"id":"https://openalex.org/C26831200","wikidata":"https://www.wikidata.org/wiki/Q16963953","display_name":"Marginal structural model","level":3,"score":0.487925261259079},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4520745277404785},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.42477500438690186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3736668825149536},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3732067942619324},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33945971727371216},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3070846199989319},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28424352407455444}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.32614/rj-2022-058","doi":"https://doi.org/10.32614/rj-2022-058","is_oa":true,"landing_page_url":"http://dx.doi.org/10.32614/rj-2022-058","pdf_url":"https://journal.r-project.org/articles/RJ-2022-058/RJ-2022-058.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/I4402554076","host_organization_name":"R Foundation for Statistical Computing","host_organization_lineage":["https://openalex.org/I4402554076"],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The R Journal","raw_type":"journal-article"},{"id":"pmid:39310290","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/39310290","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The R journal","raw_type":"Journal Article"},{"id":"pmh:oai:pubmedcentral.nih.gov:11415261","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11415261","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"R J","raw_type":"Text"},{"id":"pmh:oai:arXiv.org:2110.10276","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.10276","pdf_url":"https://arxiv.org/pdf/2110.10276","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3206699728","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2110.10276","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2110.10276","doi":"https://doi.org/10.48550/arxiv.2110.10276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2110.10276","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.32614/rj-2022-058","doi":"https://doi.org/10.32614/rj-2022-058","is_oa":true,"landing_page_url":"http://dx.doi.org/10.32614/rj-2022-058","pdf_url":"https://journal.r-project.org/articles/RJ-2022-058/RJ-2022-058.pdf","source":{"id":"https://openalex.org/S2489169438","display_name":"The R Journal","issn_l":"2073-4859","issn":["2073-4859"],"is_oa":true,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","dongbi-c","jufo-1","ki-jl-1","norway-1","poland-100","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/I4402554076","host_organization_name":"R Foundation for Statistical Computing","host_organization_lineage":["https://openalex.org/I4402554076"],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The R Journal","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.5683000087738037}],"study_designs":[],"awards":[{"id":"https://openalex.org/G6833215819","display_name":"Flexible Bayesian approaches to causal inference with multilevel survival data and multiple treatments","funder_award_id":"R21CA245855","funder_id":"https://openalex.org/F4320337351","funder_display_name":"National Cancer Institute"},{"id":"https://openalex.org/G9000893625","display_name":"Bayesian machine learning for causal inference with incomplete longitudinal covariates and censored survival outcomes","funder_award_id":"R01HL159077","funder_id":"https://openalex.org/F4320337338","funder_display_name":"National Heart, Lung, and Blood Institute"}],"funders":[{"id":"https://openalex.org/F4320337338","display_name":"National Heart, Lung, and Blood Institute","ror":"https://ror.org/012pb6c26"},{"id":"https://openalex.org/F4320337351","display_name":"National Cancer Institute","ror":"https://ror.org/040gcmg81"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3206699728.pdf","grobid_xml":"https://content.openalex.org/works/W3206699728.grobid-xml"},"referenced_works_count":53,"referenced_works":["https://openalex.org/W402407836","https://openalex.org/W1972851018","https://openalex.org/W1978108654","https://openalex.org/W1982974716","https://openalex.org/W2002111751","https://openalex.org/W2031009385","https://openalex.org/W2048470090","https://openalex.org/W2059796825","https://openalex.org/W2060316606","https://openalex.org/W2064903582","https://openalex.org/W2100824656","https://openalex.org/W2122771444","https://openalex.org/W2126292488","https://openalex.org/W2132917208","https://openalex.org/W2142495742","https://openalex.org/W2152849583","https://openalex.org/W2166303835","https://openalex.org/W2263423262","https://openalex.org/W2271345396","https://openalex.org/W2314734263","https://openalex.org/W2322316314","https://openalex.org/W2511521172","https://openalex.org/W2580460079","https://openalex.org/W2589446704","https://openalex.org/W2599076725","https://openalex.org/W2735301415","https://openalex.org/W2761665523","https://openalex.org/W2780227674","https://openalex.org/W2883724551","https://openalex.org/W3003146665","https://openalex.org/W3031346732","https://openalex.org/W3094334906","https://openalex.org/W3094852588","https://openalex.org/W3099006712","https://openalex.org/W3119598895","https://openalex.org/W3122049309","https://openalex.org/W3122843734","https://openalex.org/W3145147359","https://openalex.org/W3173171400","https://openalex.org/W3176085565","https://openalex.org/W3209332809","https://openalex.org/W4221139146","https://openalex.org/W4233056867","https://openalex.org/W4233471163","https://openalex.org/W4247399381","https://openalex.org/W4252507878","https://openalex.org/W4287238731","https://openalex.org/W4399546732","https://openalex.org/W4399578123","https://openalex.org/W4399587853","https://openalex.org/W4399635924","https://openalex.org/W4399648474","https://openalex.org/W6868892770"],"related_works":["https://openalex.org/W3184176530","https://openalex.org/W3094731521","https://openalex.org/W3112636474","https://openalex.org/W3173171400","https://openalex.org/W3031346732","https://openalex.org/W7221428246","https://openalex.org/W6998952236","https://openalex.org/W4402533220","https://openalex.org/W4323286949","https://openalex.org/W2791886957"],"abstract_inverted_index":{"[*CIMTx*](https://CRAN.R-project.org/package=CIMTx)":[0,58,80,101,132],"provides":[1,133],"efficient":[2],"and":[3,51,95,124],"unified":[4],"functions":[5],"to":[6,68,88,104,141,151],"implement":[7],"modern":[8],"methods":[9,26],"for":[10,110],"causal":[11,92,144],"inferences":[12],"with":[13,19,41],"multiple":[14,75],"treatments":[15],"using":[16,114],"observational":[17],"data":[18,66,71],"a":[20,83,134],"focus":[21],"on":[22],"binary":[23],"outcomes.":[24],"The":[25],"include":[27],"regression":[28,37,39,122],"adjustment,":[29],"inverse":[30,115],"probability":[31,116],"of":[32,44,86,117,154],"treatment":[33,76,118,158],"weighting,":[34,119],"Bayesian":[35,120],"additive":[36,121],"trees,":[38],"adjustment":[40],"multivariate":[42],"spline":[43],"the":[45,69,74,79,90,98,106,129],"generalized":[46],"propensity":[47],"score,":[48],"vector":[49,125],"matching":[50],"targeted":[52],"maximum":[53],"likelihood":[54],"estimation.":[55],"In":[56],"addition,":[57],"illustrates":[59],"ways":[60],"in":[61,73,149],"which":[62],"users":[63],"can":[64],"simulate":[65],"adhering":[67],"complex":[70],"structures":[72],"setting.":[77],"Furthermore,":[78],"package":[81],"offers":[82],"unique":[84],"set":[85],"features":[87],"address":[89],"key":[91],"assumptions:":[93],"positivity":[94,99],"ignorability.":[96],"For":[97],"assumption,":[100,131],"demonstrates":[102],"techniques":[103],"identify":[105],"common":[107],"support":[108],"region":[109],"retaining":[111],"inferential":[112],"units":[113],"trees":[123],"matching.":[126],"To":[127],"handle":[128],"ignorability":[130],"flexible":[135],"Monte":[136],"Carlo":[137],"sensitivity":[138],"analysis":[139],"approach":[140],"evaluate":[142],"how":[143],"conclusions":[145],"would":[146],"be":[147],"altered":[148],"response":[150],"different":[152],"magnitude":[153],"departure":[155],"from":[156],"ignorable":[157],"assignment.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-03T10:50:19.383937","created_date":"2021-10-25T00:00:00"}
